This application, in response to RFA CA-93-035, proposes to implement and disseminate a pharmacologic algorithm for breast cancer pain management. Many national organizations such as the Agency for Health Care Policy and Research (AHCPR) and the American Pain Society (APS), as well as international organizations such as The World Health Organization (WHO) and the International Association for the Study of Pain (IASP) have advocated for the use of "guidelines" or "standards" that are based on a step ladder pharmacologic decision making model. This proposal will test a pharmacologic algorithm utilizing well established and recommended drug therapies, based on pain assessment, applied in the breast cancer population, with medication dosing and titration and side effect management by physicians and nurses in outpatient centers with facilitation by home care nurses. The first phase of the study will test the feasibility of the algorithm. Eight outpatient oncology clinics with 28 medical oncologists and their nurses (MD/RN) will participate. Breast cancer patients with evidence of metastatic or invasive disease will be randomized and assigned to algorithm or no-algorithm treatment. Patients in both groups will receive an initial comprehensive pain evaluation, side effect evaluation and baseline outcome studies. Randomzation will occur after evaluations are completed. Patients in the algorithm group will be placed into the algorithm based on pain assessment and previous opioid therapy and initiated on pharmacologic therapy as dictated by the algorithm by the research team. Pain reassessment by the clinic or home care staff will also be dictated by the algorithm. Patients in the no- algorithm group will receive the "usual and customary" pain management care as prescribed by their primary oncology MD/RN. Outcome measures examining pain, symptom distress, and quality of life will be collected at the end of months 1,2,3, and 6, from both groups. As interim analysis will be performed at the end of phase I. Information from the phase I experience and expert consultation from leaders in the cancer pain management field will result in the planning and implementation of a algorithm training session for participating oncologists and oncology nurses. MD/RN pairs will be randomized to algorithm or no-algorithm groups. Algorithm MD/RNs will attend a four hour training session followed by a one time educational reinforcement session. The 2nd phase of the study will examine the ability of the research team to transfer knowledge of the algorithmic process successfully into the hands of community physicians and nurses. We hypothesize that l) patients receiving cancer pain treatment utilizing an algorithmic treatment decision model will report less pain, fewer side effects, and enhanced quality of life than patients receiving cancer pain treatment without the decision model and 2) MD/RN teams that receive specialized training in the use of an algorithmic decision model will be able to successfully transfer that knowledge of the algorithmic decision making model into their practice.